D2.disc {biotools}

Discriminant Analysis Based on Mahalanobis Distance

Description

A function to perform discriminant analysis based on the squared generalized Mahalanobis distance (D2) of the observations to the center of the groups.

Usage

## Default S3 method:
D2.disc(data, grouping, pooled.cov = NULL)
## S3 method for class 'D2.disc'
print(x, ...)
## S3 method for class 'D2.disc'
predict(object, newdata = NULL, ...)

Arguments

data

a numeric data.frame or matrix (n x p).

grouping

a vector of length n containing the class of each observation (row) in data.

pooled.cov

a grouping-pooled covariance matrix (p x p). If NULL (default), D2.disc will automatically compute a pooled covariance matrix.

x, object

an object of class "D2.disc".

newdata

numeric data.frame or matrix of observations to be classified. If NULL (default), the input data used as argument in D2.disc will be used.

...

further arguments.

Value

A list of

call

the call which produced the result.

data

numeric matrix; the input data.

D2

a matrix containing the Mahalanobis distances between each row of data and the center of each class of grouping. In addition, the original and the predicted (lowest distance) class are displayed, as well as a chacater vector indicating where the misclassification has occured.

means

a matrix containing the vector of means of each class in grouping.

pooled

the pooled covariance matrix.

confusion.matrix

an object of class confusionmatrix.

Author(s)

Anderson Rodrigo da Silva <[e-mail]>

References

Manly, B.F.J. (2004) Multivariate statistical methods: a primer. CRC Press. (p. 105-106).

Mahalanobis, P.C. (1936) On the generalized distance in statistics. Proceedings of The National Institute of Sciences of India, 12:49-55.

See Also

D2.dist, confusionmatrix, lda

Examples

data(iris)
(disc <- D2.disc(iris[, -5], iris[, 5]))
first10 <- iris[1:10, -5]
predict(disc, first10)
predict(disc, iris[, -5])$class

# End (not run)